Currently submitted to: Journal of Medical Internet Research
Date Submitted: May 6, 2026
Open Peer Review Period: May 6, 2026 - Jul 1, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Perceptions, Responsibility, and Implementation of AI-CDSS in VTE Prevention: A Qualitative Study
ABSTRACT
Background:
Background:
Artificial intelligence–enabled clinical decision support systems (AI-CDSSs) are increasingly deployed for venous thromboembolism (VTE) prevention. However, healthcare professionals’ perceptions and experiences of these systems across diverse regional, occupational, and specialty contexts remain poorly understood, with limited evidence on how AI integration influences clinical workflows, responsibility allocation, and professional trust within multi‑tiered healthcare systems.
Objective:
Objective:
This study aimed to systematically investigate healthcare professionals’ perceptions and experiences of using AI-CDSS for VTE prevention across different institutional levels and clinical roles in China.
Methods:
Methods:
A nationwide qualitative study was conducted using semi‑structured interviews with 23 healthcare professionals from diverse institutional levels and clinical roles. Data collection proceeded until thematic saturation was reached. All interviews were transcribed verbatim and analyzed using inductive thematic analysis.
Results:
Five core themes were identified: (1) AI reduces workload but complicates clinical responsibility; (2) patient involvement is perceived as beneficial yet problematic; (3) digital readiness shapes implementation feasibility; (4) trust in AI varies by professional role; and (5) responsibility and risk remain ambiguous after AI introduction. Facilitating factors included clearly defined responsibility assignment, comprehensive training, incentive mechanisms, and institutional oversight. Key barriers comprised economic costs, additional workload burden, and complex hospital approval processes.
Conclusions:
Our findings reveal structural tensions arising from the interaction between professional roles, institutional readiness, and responsibility distribution during AI integration. These results underscore the need for tiered, role‑specific implementation strategies and provide practical insights for the sustainable deployment of AI in VTE prevention.
Citation
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Copyright
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